Visible to the public Ultra-wideband Fingerprinting Positioning Based on Convolutional Neural Network

TitleUltra-wideband Fingerprinting Positioning Based on Convolutional Neural Network
Publication TypeConference Paper
Year of Publication2020
AuthorsLei, M., Jin, M., Huang, T., Guo, Z., Wang, Q., Wu, Z., Chen, Z., Chen, X., Zhang, J.
Conference Name2020 International Conference on Computer, Information and Telecommunication Systems (CITS)
Date PublishedOct. 2020
ISBN Number978-1-7281-6544-8
KeywordsAcoustic Fingerprints, composability, convolutional neural nets, convolutional neural network, convolutional neural networks, fingerprint positioning methods, Fingerprint recognition, fingerprinting positioning, Global Positioning System, GPS, Human Behavior, indoor positioning system, indoor radio, precise position, precise positioning, pubcrawl, resilience, Resiliency, Robustness, satellite signals, Support vector machines, telecommunication computing, Training, ultra wideband communication, Ultra wideband technology, ultra-wideband, ultra-wideband fingerprinting positioning method, underwater acoustic communication, Wireless fidelity

The Global Positioning System (GPS) can determine the position of any person or object on earth based on satellite signals. But when inside the building, the GPS cannot receive signals, the indoor positioning system will determine the precise position. How to achieve more precise positioning is the difficulty of an indoor positioning system now. In this paper, we proposed an ultra-wideband fingerprinting positioning method based on a convolutional neural network (CNN), and we collect the dataset in a room to test the model, then compare our method with the existing method. In the experiment, our method can reach an accuracy of 98.36%. Compared with other fingerprint positioning methods our method has a great improvement in robustness. That results show that our method has good practicality while achieves higher accuracy.

Citation Keylei_ultra-wideband_2020